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Entanglement and negativity Hamiltonians for the massless Dirac field on the half line
No full textWe study the ground-state entanglement Hamiltonian of several disjoint intervals for the massless Dirac fermion on the half-line. Its structure consists of a local part and a bi-local term that couples each point to another one in each other interval. The bi-local operator can be either diagonal or mixed in the fermionic chiralities and it is sensitive to the boundary conditions. The knowledge of such entanglement Hamiltonian is the starting point to evaluate the negativity Hamiltonian, i.e. the logarithm of the partially transposed reduced density matrix, which is an operatorial characterisation of entanglement of subsystems in mixed states. We find that the negativity Hamiltonian inherits the structure of the corresponding entanglement Hamiltonian. We finally show how the continuum expressions for both these operators can be recovered from exact numerical computations in free-fermion chains
TESS-Keck Survey. XIV. Two Giant Exoplanets from the Distant Giants Survey
No full textWe present the Distant Giants Survey, a three-year radial velocity campaign to measure P(DG|CS), the conditional
occurrence of distant giant planets (DG; M_p ∼ 0.3–13 M_J, P > 1 yr) in systems hosting a close-in small planet (CS;
R_p < 10 R_⊕). For the past two years, we have monitored 47 Sun-like stars hosting small transiting planets detected by TESS. We present the selection criteria used to assemble our sample and report the discovery of two distant giant
planets, TOI-1669 b and TOI-1694 c. For TOI-1669 b we find that M sin i = 0.573 ± 0.074 M_J, P = 502 ± 16 days,
and e < 0.27, while for TOI-1694 c, M sin i = 1.05 ± 0.05 M_J, P = 389.2 ± 3.9 days, and e = 0.18 ± 0.05. We also
confirmed the 3.8 days transiting planet TOI-1694 b by measuring a true mass of M = 26.1 ± 2.2 M_⊕. At the end of the Distant Giants Survey, we will incorporate TOI-1669 b and TOI-1694 c into our calculation of P(DG|CS), a crucial
statistic for understanding the relationship between outer giants and small inner companions
Energy-Efficient Connected Cruise Control With Lean Penetration of Connected Vehicles
No full textThis paper focuses on energy-efficient longitudinal controller design for a connected automated truck that travels in mixed traffic consisting of connected and non-connected vehicles. The truck has access to information about connected vehicles beyond line of sight using vehicle-to-everything (V2X) communication. A novel connected cruise control design is proposed which incorporates additional delays into the control law when responding to distant connected vehicles to account for the finite propagation speed of traffic waves. The speeds of non-connected vehicles are modeled as stochastic processes. A fundamental theorem is proven which links the spectral properties of the motion signals to the average energy consumption. Controller synthesis for gain parameters is conducted over downstream traffic data and evaluated over a combination of synthetic and real cycles. It is demonstrated that even with lean penetration of connected vehicles, our controller can bring significant energy savings
Multimodal Characterization of Crystal Structure and Formation in Rubrene Thin Films Reveals Erasure of Orientational Discontinuities
No full textMultimodal multiscale characterization provide opportunities to study organic semiconducting thin films with multiple length scales, across multiple platforms, to elucidate crystallization mechanisms of the various microstructures that impact functionality. With polarized scanning transmission X-ray and 4D-scanning transmission electron microscopy, hybrid crystalline structures in rubrene thin films in which large crystalline domains surround a common nucleus and transition to a spherulite morphology at larger radii is observed. These high-resolution techniques reveal how azimuthal orientational discontinuities at smaller radii are erased as spherulite morphology takes hold. In situ crystallization in the films with optical microscopy is also captured, discovering the importance of considering the initial temperature increase of a film during thermal annealing over the crystallization timescale. This kinetic information of the radial crystallization rate and of corresponding film heating kinetics is used to estimate the temperature at which the larger crystalline regions transition into a spherulite. By combining the results obtained from the different characterization modes, it is learned that thermal conditions can sensitively affect the crystallization of rubrene and other organic thin films. The observations suggest opportunities for more complex temperature-dependent processing to maximize hybrid structures’ functionality in organic thin films and demonstrate that multimodal studies deepen the understanding of structure-function dynamics
Automated modal identification by quantification of high-spatial-resolution response measurements
No full textIdentifying modal parameters from vibration measurements is an essential step for modal analysis and modeling of structural dynamics. A critical challenge in modal parameter identification is the determination of the physical modes from spurious modes, especially with noisy measurement data. In this study, an approach is presented to enable automated identification of modal parameters by quantifying the spatial features of full-field, high-spatial-resolution response measurements. Specifically, it is derived that the local variances of the physical and spurious mode shapes are drastically distinguishing, especially when the spatial resolution of the response measurement is high (i.e., full-field with dense spatial measurement points). This allows an effective identification of the physical modes from spurious. Experimental studies are conducted on a few structural models and detailed comparisons are performed and discussed between the presented method and existing methods, including parametric and non-parametric
Endowing Griffith’s fracture theory with the ability to describe fatigue cracks
No full textIn this paper, a possible extension of Griffith’s fracture theory to describe fatigue-induced crack propagation is proposed. To this end, an energy-based model is presented, taking Griffith’s model as the point of departure and employing the concept of state-dependent fracture toughness. Therein, fatigue degradation is achieved through a suitable history variable endowed with a functional form that is able to consider crucial aspects of fatigue, including the crack-tip singularity, the identification of fatigue-inducing loading conditions, and mean load effects. Simple paradigmatic examples indicate that the model provides a unified description of different fatigue responses and unveils peculiar regimes in the crack propagation process, always preserving the link to classical fracture mechanics. In this context, analytical results establish a clear relation between Griffith’s fracture theory and Paris’ law. The proposed modeling framework paves the way for future developments in modern fracture mechanics, e.g., to derive a novel generation of variational fatigue phase-field fracture models suitably rooted in a Griffith-based theory
Simultaneous Millimeter-wave, Gamma-Ray, and Optical Monitoring of the Blazar PKS 2326-502 during a Flaring State
No full textIncluding millimeter-wave data in multiwavelength studies of the variability of active galactic nuclei (AGN) can provide insights into AGN physics that are not easily accessible at other wavelengths. We demonstrate in this work the potential of cosmic microwave background (CMB) telescopes to provide long-term, high-cadence millimeter-wave AGN monitoring over large fractions of sky. We report on a pilot study using data from the SPTpol instrument on the South Pole Telescope (SPT), which was designed to observe the CMB at arcminute and larger angular scales. Between 2013 and 2016, SPTpol was used primarily to observe a single 500 deg² field, covering the entire field several times per day with detectors sensitive to radiation in bands centered at 95 and 150 GHz. We use SPT 150 GHz observations to create AGN light curves, and we compare these millimeter-wave light curves to those at other wavelengths, in particular γ-ray and optical. In this Letter, we focus on a single source, PKS 2326-502, which has extensive, day-timescale monitoring data in gamma-ray, optical, and now millimeter-wave between 2013 and 2016. We find PKS 2326-502 to be in a flaring state in the first 2 yr of this monitoring, and we present a search for evidence of correlated variability between millimeter-wave, optical R-band, and γ-ray observations. This pilot study is paving the way for AGN monitoring with current and upcoming CMB experiments such as SPT-3G, Simons Observatory, and CMB-S4, including multiwavelength studies with facilities such as Vera C. Rubin Observatories Large Synoptic Survey Telescope
Real-time high-resolution CO₂ geological storage prediction using nested Fourier neural operators
No full textCarbon capture and storage (CCS) plays an essential role in global decarbonization. Scaling up CCS deployment requires accurate and high-resolution modeling of the storage reservoir pressure buildup and the gaseous plume migration. However, such modeling is very challenging at scale due to the high computational costs of existing numerical methods. This challenge leads to significant uncertainties in evaluating storage opportunities, which can delay the pace of large-scale CCS deployment. We introduce Nested Fourier Neural Operator (FNO), a machine-learning framework for high-resolution dynamic 3D CO₂ storage modeling at a basin scale. Nested FNO produces forecasts at different refinement levels using a hierarchy of FNOs and speeds up flow prediction nearly 700 000 times compared to existing methods. By learning the solution operator for the family of governing partial differential equations, Nested FNO creates a general-purpose numerical simulator alternative for CO₂ storage with diverse reservoir conditions, geological heterogeneity, and injection schemes. Our framework enables unprecedented real-time modeling and probabilistic simulations that can support the scale-up of global CCS deployment
The motion of a layer of yield-stress material on an oscillating plate
No full textThe motion of a finite layer of Bingham material on a solid plate that executes in-plane oscillations was reported previously by Balmforth et al. (J. Non-Newtonian Fluid Mech., vol. 158, issue 1–3, 2009, pp. 46–53). There, it was suggested that multiple yielded regions may arise within the material; this contrasts to start-up flow of the same material for which only one yielded region is generated. Here, we explore quantitatively the fluid physics of this oscillatory flow problem through analytical approximations and further numerical computation. Four new key topological properties concerning the generation and termination of the yielded regions are reported. It is shown that the existence of multiple yielded regions is equivalent to the layer never becoming entirely rigid during the periodic motion. For small inertia, the flow is approximately time-reversible with only a single yielded region generated at the plate. For large inertia, shear stress in the material decays rapidly as a function of distance from the plate. A thin zone of yielded material detaches periodically from the plate, and subsequently terminates within the layer. At high oscillation frequency, there can be any number N of distinct rigid regions, satisfying N = [1 - π⁻¹ log B] where B is the Bingham number. It is also shown that for B > 0.5370, there are at most one yielded region and one rigid region throughout the motion. These theoretical results can be used as a basis for oscillatory rheometry, allowing for measurement of the yield stress using existing apparatus
Prismer: A Vision-Language Model with An Ensemble of Experts
No full textRecent vision-language models have shown impressive multi-modal generation capabilities. However, typically they require training huge models on massive datasets. As a more scalable alternative, we introduce Prismer, a data- and parameter-efficient vision-language model that leverages an ensemble of domain experts. Prismer only requires training of a small number of components, with the majority of network weights inherited from readily-available, pre-trained domain experts, and kept frozen during training. By leveraging experts from a wide range of domains, we show that Prismer can efficiently pool this expert knowledge and adapt it to various vision-language reasoning tasks. In our experiments, we show that Prismer achieves fine-tuned and few-shot learning performance which is competitive with current state-of-the-art models, whilst requiring up to two orders of magnitude less training data. Code is available at https://github.com/NVlabs/prismer